Because the structure of the moving target in the air is complex and its composition is not single, it is impossible to simply regard the measured spectral signal as the sum of all parts. For spectral acquisition of complex small target in salient region, a target saliency region unmixing method is innovatively proposed in this paper. The abundance information of sky background, target region and salient regions was obtained by analyzing the infrared image. According to the measured spectra and their weighted linear combination relationship, the precise spectral signal of the target saliency region can be estimated. The experimental results show that, through this method, the spectra of each salient regions on the target is more accurate and have more abundant details. The comprehensive judgment result of each salient regions spectra can effectively improve the target recognition accuracy.
Because the spatial resolution of remote sensing equipment is low, how to accurately obtain the true spectrum of the target to guide the stable tracking of the target is a complex problem. This paper proposes an air moving target tracking method based on spectral unmixing. Firstly the target abundance is obtained through the pretreatment of infrared image, secondly, accurate spectrum of target is acquired based on the linear combination relationship between the background spectrum and the measured spectrum, and then corrected by the atmosphere, that is, the object side is obtained. Accurate spectrum. Through the spectral feature comparison, the target tracking is guided according to the fingerprint information of the precise spectrum. A large number of field experiments prove that this method can accurately measure the weak targets in the remote sensing background, improve the stability and accuracy of tracking, and lay a foundation for the subsequent remote sensing target recognition.
When the small targets are dim and with dark-spots interference in infrared images, the targets can not be accurately detected by multi-level filter. In order to solve this problem, the dark-spots filtering algorithm based on contrast suppression is added to the original multi-level filter for detecting small dim targets. First, the result of difference image based on the low-pass filtered image and the original image preserves the information of the targets and dark-spots at the same time. Second, the difference image and the original image are superimposed to remove dark-spots, according to the inhibitory factor determined by the contrast between dark-spots and background. Finally, the targets are enhanced by using Gamma correction. The experimental results demonstrate that the algorithm enhances the targets, removes dark-spots, and suppresses the background of false detection, so the multi-level filter adding the proposed method can effectively detect small dim targets.
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